1. Field of the Invention
The present invention relates to a multiphotometric apparatus, and, more particularly to a multiphotometric apparatus which divides an object field into a plurality of regions to measure the luminance of each region so as to determine the optimum exposure value by using a fuzzy inference performed according to the output which denotes the measured luminance of each region.
2. Related Background Art
A conventional multiphotometric apparatus detects, as input values, luminance value BVO at the central portion of the frame, maximum luminance value BVmax, minimum luminance value BVmin, maximum luminance difference dBV, mean luminance BVmean from a plurality of outputs denoting the results of the measurements of the luminance obtained by dividing the object field into a plurality of regions and by measuring the luminance of each region. The input values thus detected are given boundary values and are combined to one another so as to select an exposure value and to make it to be the optimum exposure value, the exposure value being a value calculated by any one of calculating equations corresponding to, for example, four photometric methods, that is the mean photometry BVmean, the central value photometry BVO, high luminance weighted photometry (BVmean+BVmax)/2 and low luminance weighted photometry (BVmean+BVmin)/2.
In this case, the boundary values for selecting the aforesaid calculation equations are determined by, for example as shown in FIG. 10, providing photometric areas by calculating equations according to the values of the luminance difference dBV and the maximum luminance value BVmax. Referring to FIG. 10, the luminance difference dBV and the maximum luminance value BVmax are respectively divided into 5 stages and low luminance weighted photometric area L, central portion weighted photometric area C, mean photometric area M and high luminance weighted photometric area H are determined as illustrated according to each stage. For example, in a case where the luminance difference dBV is a middle value and the maximum luminance value is a relatively dark object of a degree such as an evening scene or an indoor object, the mean photometry is selected.
However, the conventional multiphotometric apparatus has no means for overcoming a problem that takes place relating to input values adjacent to the boundary regions between photometric areas. That is, there sometimes arises a case in which calculation equations BVmeans of the mean photometric area M and (BVmeans+ BVmax) of the high luminance weighted photometric area H having different output values are positioned adjacently. In this case, when an object positioned in the vicinity of the boundary value is photographed, there arises a problem in that the former or the latter calculation equation is selected occasionally.
In order to overcome a problem of the type described above, a camera has been disclosed in, for example, Japanese Patent Laid-Open No. 3-17636 and is arranged to determine an exposure value by adding weight coefficients to a plurality of photoelectrically converted outputs according to a fuzzy inference.
However, the aforesaid conventional camera encounters a problem in that the photometric method for determining the exposure value is limited because its photometric means, which generates a plurality of photoelectrically converted outputs corresponding to the luminance distribution of the object, determines the exposure value by directly adding the weighting coefficients corresponding to the results of the fuzzy inference to the aforesaid photoelectrically converted outputs. Another problem arises in that the exposure cannot be determined by a plurality of photometric methods. If it can be determined, an excessively complicated process must be performed.
On the other hand, neural networks are known which exhibit excellent pattern recognizing performance and a photometric apparatus for a camera has been suggested (Japanese Patent Laid-Open No. 2-96723) which receives a photometric output divided by the neural network and in which the weight of coupling of the network is previously determined by learning and the output of the network is made to be the exposure value in a state where the weight of coupling is fixed when a microcomputer mounted on the camera. By properly structuring the network, a substantially proper exposure value of the subject having the learned pattern can be obtained.
However, the aforesaid conventional photometry calculating method encounters a problem in that the exposure value becomes unstable due to a slight change in the frame if the object is positioned at a boundary of the estimated photography scene.
In order to be adapted to a multiplicity of photographic scenes, there arises a necessity of classifying into a further large number of patterns, causing the selection of the optimum exposure value to become complicated. Therefore, a photographic scene, which has not been estimated, cannot be predicted and therefore a value, which is excessively different from the proper exposure value, is undesirably obtained.
The network for weighting the photographic scene must be properly structured in order to obtain a substantially proper exposure value for the estimated photographic scene. In order to obtain this, the size of the network cannot be reduced and the microcomputer mounted on the camera cannot satisfactorily process the required quantity of calculations and an excessively large number of data items are required to classify the pattern.